Correct diagnosis and effective treatment begin with a complete understanding of what a patient has experienced and is experiencing health-wise. It’s more challenging to do though when the data needed for drawing a holistic picture of one’s health is stored across a range of legacy systems that do not communicate that well.
Having to navigate siloed data also causes healthcare professionals to lose 9 hours of work time per week.
Brining all healthcare data into a single, consolidated storage — a data warehouse — can help eliminate data discrepancy and speed up clinical and administrative processes.
In this article, we spotlight everything you need to know about a healthcare data warehouse before turning to data warehouse consulting.
What is a healthcare data warehouse?
A healthcare data warehouse is a centralized storage for all the healthcare data gathered from various sources. These sources could span electronic medical records (EMR), electronic health records (EHR), lab and radiology databases, healthcare and fitness wearables, and others. The key value of a data warehouse is that it stores data in unified format, so it can be used for analytics right away.
The healthcare data warehousing market overview
The global healthcare data storage market is projected to reach $7.7 billion by 2030 growing at a CAGR of 13.9% from 2022, Verified Market Research found. It is primarily due to the following factors that healthcare industry players are getting more interested in adopting centralized data storage solutions:
As a result, more healthcare organizations around the globe are switching to data warehouses. Specifically, they look for ways to manage the ever-growing volumes of data, leverage predictive and prescriptive analytics, and automate clinical processes.
Why adopt a healthcare data warehouse?
Three major factors constitute the value of integrated healthcare data storage:
And if you are looking for specific examples of how caregivers can build on the capabilities of centralized data storage, here are several use cases that demonstrate just that:
The innards of a healthcare data warehouse
The standard architecture of a data warehouse for healthcare comprises four layers:
A data source layer that comprises clinical, lab, research, sensor, and other data from internal and external sources.
A staging layer that transforms the data from multiple sources into a single, consistent, and coherent body through the extract, transform, load (ETL) or extract, load, transform (ELT) process.
An integrated data storage layer that contains data related to multiple subject areas or is made up of subsets assigned to specific departments or areas of clinical practice, also called data marts.
A data analytics layer that features tools for descriptive, predictive, and prescriptive analytics, as well as reporting and visualization.
Features to prioritize when building a healthcare data warehouse
Healthcare data is sensitive by nature, so it needs to be handled properly. To ensure patient safety and protect healthcare providers from legal risks, a healthcare data warehouse needs to meet specific requirements. Here are a few features you should pay attention to:
Data security and compliance
In accordance with federal and state laws, organizations that manage healthcare data must implement security safeguards to protect personally identifiable information. Here are some ways to make sure the data is secure:
Establishing a data management strategy and data governance procedures to protect personal information from being misused. Creating read-only replicas, setting up custom user groups with predefined access rights, or encrypting personal info for data governance are trusted ways to do that.
Implementing row-level permissions to restrict certain users from viewing certain data points. You could set up row-level permissions by account or patient ownership, for instance, to give a particular doctor access to their patients' records, but not all doctors.
Setting up permissions at the data analytics level to guarantee confidential data won’t be exposed via a dashboard or report.
In order for a warehouse to serve its purpose, the data it holds must be clear, accurate, relevant, and transformed to fit an established model. ETL and ELT processes help maintain data integrity. With ETL, data is modified before it reaches the target system, usually at the staging layer. With ELT, in contrast, data is transformed at the warehouse's storage layer. An organization should prioritize either ETL or ELT depending on what type of healthcare solutions it runs on top of a data warehouse.
Data warehouse performance
Reliable performance is crucial when it comes to handling health-related data, especially that retrieved from connected medical devices. The following features can ensure faster and more consistent transmission, querying, and retrieval of data:
Crucial integrations to introduce
Healthcare data warehouses are most effective when they're part of a broader ecosystem. When turning to healthcare solutions development, consider planning for these vital integrations:
A data lake. Data lakes may serve as a data source for training machine learning models, as they store unstructured and semi-structured data.
Business intelligence. BI solutions may use the cleansed, structured data stored in the data warehouse to generate descriptive analytics insight and support decision-making.
Machine learning. Machine learning could improve diagnosis and treatment, as well as hospital operations, by enabling predictive and prescriptive analytics.
A path to implementing a healthcare data warehouse
The data warehouse implementation process can be broken down into the following four steps:
Planning. The planning stage centers around thinking out the strategic aspects of adopting a data warehouse. The tasks to accomplish include defining stakeholder needs, identifying bottlenecks in the data management process, assessing the available IT infrastructure, formulating the strategic objectives, putting together a vision for the future warehouse, as well as planning the needed resources.
Design. At the design stage, one would usually put down a data warehouse architecture, design the necessary integrations, and think through the data warehouse data model.
Development and implementation. The focus of the development stage is to implement the necessary infrastructure components and to develop and implement data warehousing software and end-user applications.
Testing, including post-migration testing. In addition to ongoing testing, additional validation is needed after data migration. The migrated data should be checked for duplicates, errors, contradictions, and inaccuracies.